CN114545387A - High-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting - Google Patents
High-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting Download PDFInfo
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- CN114545387A CN114545387A CN202210173162.XA CN202210173162A CN114545387A CN 114545387 A CN114545387 A CN 114545387A CN 202210173162 A CN202210173162 A CN 202210173162A CN 114545387 A CN114545387 A CN 114545387A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/581—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of interrupted pulse modulated waves and based upon the Doppler effect resulting from movement of targets
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting. The method comprises the steps of monitoring a suspicious parabolic area of a high-rise building in real time through a millimeter wave radar system to obtain a distance-Doppler detection track of a moving target, using a distance-Doppler characteristic curve of the moving target in radar detection for judging high-altitude parabolic behaviors, and judging whether the high-altitude parabolic behaviors occur or not by fitting the moving target distance-Doppler track after clutter suppression and a simulation characteristic result of classical mechanics parabolic motion. The invention effectively provides a method for judging the high-altitude parabolic track by the millimeter wave radar, and greatly improves the feasibility and reliability of evaluating the high-altitude parabolic behavior.
Description
Technical Field
The invention relates to the field of millimeter wave radar detection and city safety, in particular to a high-altitude parabolic detection discrimination method based on millimeter wave radar data fitting.
Background
The monitoring of the high-altitude parabolic motion is the key for preventing and frightening the high-altitude parabolic motion, and related researches are carried out on the problem in both academic circles and industrial circles. The existing high-altitude parabolic monitoring means is realized based on a visual detection technology. However, high altitude parabolic monitoring schemes based on visual dominance are very susceptible to environmental factors, and the performance is severely deteriorated in dark light (at night) or extreme weather environments (fog, dust, etc.).
The basic principle of radar determines that it has natural advantages for monitoring moving objects. The millimeter wave radar works in a millimeter wave (30-300GHz) frequency band, can work all weather and has extremely high spatial resolution due to small volume, is rapidly developed in recent years, is widely applied to detection of moving targets, and has great potential in detection of various high-altitude parabolas. However, due to the characteristics of a complex electromagnetic environment in a city, the millimeter wave radar can detect other interference targets unrelated to high-altitude parabolas, and a greater challenge is provided for the determination of high-altitude parabolic behaviors.
For millimeter wave radar, the extraction of the motion characteristic of the high-altitude parabolic target and the radar distance-Doppler characteristic curve has great significance for improving the capacity of judging the high-altitude parabolic behavior.
Disclosure of Invention
Aiming at the defects of the detection research of the current millimeter wave radar on the high-altitude parabolic target and the lack of effective means for distinguishing the parabolic target from the interference target, the invention provides that the distance-Doppler characteristic curve of the target in the radar detection is used for distinguishing the high-altitude parabolic behavior, and the reliability of the high-altitude parabolic distinguishing is improved by comparing the distance-Doppler characteristic of the classical mechanics parabolic motion.
In order to achieve the technical purpose, the technical scheme of the invention is as follows: a high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting comprises the following steps:
monitoring a suspicious parabolic area of the high-rise building in real time through a millimeter wave radar system;
sampling and processing radar echo signals to obtain a distance-Doppler detection track of a moving target;
adjusting parameters of the parabolic model, and fitting the parameters with the distance-Doppler detection track of the moving target to obtain a fitting error;
and setting a parabolic discriminant equation according to the fitting error, and judging whether the moving target is a parabolic target or not by using the parabolic discriminant equation.
Further, the millimeter wave radar system is installed on the lateral ground of the building, and the detection beam direction of the radar antenna is vertical to the upper direction and is parallel to the plane of the building wall.
Furthermore, the millimeter wave radar system adopts a single antenna to transmit signals and four linearly arranged antennas with the same interval to receive the signals; and the transmitting signal of the millimeter wave radar system is a linear frequency modulation continuous wave signal.
Further, the frequency band of 76-81GHz is adopted by the linear frequency modulation continuous wave signal.
Further, radar echo signals are processed through two times of fast Fourier transform, background interference points and noise are processed through a high-pass filter and a constant false alarm algorithm, and then the distance-Doppler detection track of the moving target is obtained through clustering.
Further, the high-pass filter adopts an FIR high-pass filter; the constant false alarm algorithm adopts a unit maximum constant false alarm method.
Further, the parabolic model is a classical mechanical parabolic model considering a moving object as a particle.
Further, fitting by using a least square method to obtain a simulated distance-Doppler image; calculating the mean square error of the actually detected point coordinates in the range-Doppler image and the sampling point coordinates of the simulated range-Doppler image, and taking the minimum value of the mean square error as a fitting error; and calculating and determining coefficients by using the residual square sum of the actual detection points and the fitting points and the regression square sum of all the detection points.
Further, the parabolic discriminant equation is:
p＝β·(R2+τ·ε+ψ(n))
wherein beta represents a monitoring coefficient, tau represents an error normalization coefficient, epsilon represents a fitting error, and R2Indicating the determination of the coefficients, n being the number of significant points in the satisfied parabolic trajectory, ψ (n) being the normalized limiting function.
Further, after the radar detects a target, a value p of a parabolic discriminant equation is calculated in real time, and when the value p is greater than 1, the high-altitude parabolic behavior is determined, an alarm signal is sent out, and data of a corresponding multi-frequency-modulation period are recorded; and when p is less than 1, judging as an interference target, and continuously monitoring and updating the p value.
The invention has the beneficial effects that: the invention provides a high-altitude parabolic detection distinguishing method based on millimeter wave radar data fitting, which is matched with a radar clutter suppression method, and effectively extracts the distance-Doppler track of a detected target object by analyzing a distance-Doppler response diagram of radar echo. The method for judging the high-altitude parabolic track by the millimeter wave radar is provided by combining a classical mechanical parabolic model and by means of distance-Doppler curve fitting comparison of theoretical simulation and actual detection, and the reliability of the millimeter wave radar for judging the high-altitude parabolic behavior can be effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a specific embodiment of a high altitude parabolic detection discrimination method based on millimeter wave radar data fitting;
FIG. 2 is a graph of parabolic range-Doppler response obtained by detecting and removing a large number of interference points within a radar multi-cycle in accordance with one embodiment of the method of the present invention;
FIG. 3 is a range-Doppler detection curve obtained by direct clustering in accordance with one embodiment of the present invention;
FIG. 4 is a graph comparing a simulated range-Doppler curve with minimal fitting error with an actual detection curve according to an embodiment of the method of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The technical method of the present invention will be further explained with reference to the drawings in the embodiments of the present invention. Based on the embodiments of the present invention, it is obvious to those skilled in the art that other embodiments obtained without inventive labor are within the scope of the present invention.
One embodiment of the present invention, as shown in fig. 1, provides a high altitude parabolic detection and discrimination method based on millimeter wave radar processing, which comprises the following steps:
(1) arranging a millimeter wave radar system: erecting a millimeter wave radar system in a high-rise building monitoring area;
the millimeter wave radar system is installed on the ground near the north and south sides of the building, and the detection beam direction of the radar antenna is vertical and upward and parallel to the plane of the building wall.
Furthermore, the millimeter wave radar system adopts a scheme that a single antenna transmits signals and four linearly arranged antennas with the same interval receive the signals. The echoes received by the four antennas can be subjected to incoherent accumulation processing to improve the signal-to-noise ratio, and can be used for analyzing the orientation of a subsequent parabolic target.
Further, the transmission signal of the millimeter wave radar system is a chirp continuous wave signal with adjustable bandwidth in a 76-81GHz frequency band.
(2) Sampling radar echo signals with a single frequency modulation period: and the millimeter wave radar system transmits a signal to monitor a suspicious parabolic area of the high-rise building, and performs down-conversion demodulation and real-time digital sampling on the radar echo to obtain original sampling data of the radar echo.
(3) Radar echo signal processing of multiple frequency modulation periods: obtaining a range-Doppler response diagram containing a parabolic target and a background interference target by two times of fast Fourier transform on original complex signal data obtained by sampling echo signals of a plurality of frequency modulation periods; and filtering static background clutter interference through a high-pass filter, and detecting by using a Constant False Alarm Rate (CFAR) algorithm to obtain a range-Doppler response diagram with only a few interference points left so as to obtain a range-Doppler detection track of the moving target under multi-pulse. Specifically, since the parabolic objects cannot be regarded as ideal particles, when there is a high-altitude parabolic target, usually a plurality of detection points on the range-doppler response map correspond to the target, and a reasonable detection target track on the range-doppler map is obtained by performing clustering processing analysis on the range-doppler response map with only a few remaining interference points.
Specifically, the background interference target may be disturbance of the environment within the detection range, such as fluttering of clothes aired on a balcony, leaf shaking and the like; or a non-parabolic target, such as a bird, present in the detection range.
Specifically, the constant false alarm algorithm adjusts a detection threshold of the radar system, keeps the false detection probability constant, and determines whether the detection signal is a target or noise by comparing the signal intensity with the detection threshold.
The Constant False Alarm Rate (CFAR) algorithm comprises a unit average method (CA-CFAR), a unit maximum value method (GO-CFAR), an ordered statistical method (OS-CFAR) algorithm and the like.
Further preferably, referring to fig. 2, in the embodiment of the present invention, the radar echo signals in multiple frequency modulation periods are processed by a cell maximum constant false alarm rate (GO-CFAR) method and an FIR high-pass filter (Finite Impulse Response filter), so as to obtain a range-doppler Response diagram of the parabolic moving target from which a large number of interference points are removed. The amplitudes of all the range-doppler response maps obtained by processing are superposed to obtain a more obvious range-doppler response variation process, which is shown in fig. 2.
Further preferably, in the embodiment of the present invention, a direct clustering method is adopted for all existing range-doppler response maps, and the target center position in each range-doppler map is obtained through processing, so that a reasonable target range-doppler trajectory can be known, as shown in fig. 3.
(4) Simulation and fitting of parabolic distance-Doppler characteristics: when the detected moving target distance-Doppler track exists, simulating the parabolic motion of a simulated classical mechanical particle target, fitting the distance-Doppler track obtained by simulation, and adjusting model parameters to obtain a simulated track with the minimum fitting error with the detected track;
specifically, a parabolic falling model is established based on classical mechanics, and an ideal range-Doppler track simulation result is obtained according to the radial distance and speed from a detection target to a radar position.
Further preferably, the parabolic model has the following differential equation form, and the air resistance of the object is in a quadratic proportional relation with the speed, specifically:
Specifically, the distance-Doppler simulation characteristic track and the detection track adopt a least square method, and parameters of a fitting model are adjusted to complete fitting. And fitting to obtain a simulated distance-Doppler track.
Further, the fitting error uses mean square error estimation to ensure that the simulated and actually detected range-doppler curve differences of the tuning parameters are minimal, i.e.
(rdet,vdet) For the coordinates of points actually detected in the range-Doppler plot, rdetSet of distances actually detected, vdetIs the set of actually detected velocities, (r)sim,vsim) For the coordinates of the sampling points, r, of the range-Doppler plot obtained in the parabolic simulationsimSet of distances calculated for the simulation, vdetFor the set of simulated velocities, n is the number of detection points in the range-doppler plot. Recording the least square method to obtain a fitting model and a determination coefficient R of the fitting result2And the fitting error epsilon.
Specifically, the fitting error ε and the determination coefficient are R2The general expression is:
the numerator of the second term in the formula (3) is the residual square sum of all the actual detection points and all the fitting points, and the denominator is the regression square sum of all the actual detection points and the average value of the detection points.
In the embodiment of the present invention, referring to FIG. 4, the actual detection node of the range-Doppler trace is shownThe fruit and the best fit result. Fitting an optimal resistance coefficient alpha of 1.1718, an optimal initial target height of 17.75m and an optimal initial parabolic speed of 3.12m/s, and determining a coefficient R20.87 and a fitting error epsilon of 0.42.
Further, through subsequent range-Doppler diagram track simulation of various parabolic objects, radar feature sets of various objects can be obtained, and accuracy of high-altitude parabolic behavior judgment is guaranteed.
(5) And (3) carrying out parabolic discrimination by using a fitting error result: setting a parabolic discrimination equation according to the requirement of high-altitude parabolic monitoring of an actual building and the fitting error, if the equation value is larger than a set reasonable threshold value, judging the high-altitude parabolic behavior, sending a warning signal, recording data of corresponding multi-frequency-modulation period, and providing analysis data for the estimation of the subsequent parabolic floor; and if the obtained fitting error is smaller than the interference threshold, judging as the interference target.
Specifically, combining the obtained goodness-of-fit parameter, a high-altitude parabolic decision equation is set as follows:
p＝β·(R2+τ·ε+ψ(n)) (4)
wherein beta represents a monitoring coefficient which can be set between 0.3 and 0.4 according to building monitoring requirements, a high-risk high-frequency building is set to be 0.4, and a low-risk low-frequency building is set to be 0.3. τ represents an error normalization coefficient, which is related to the radar erection environment and the type of the parabola, and can be measured according to a known parabola experiment in the actual process, and can be set to 0.1 in general. n is the number of significant points in the parabolic trajectory (also the number of times the target appears in the radar RD map), ψ (n) is the normalized limiting function Sigmoid, the formula of which is:
further, after the radar detects a target, a value p of a parabolic discriminant equation is calculated in real time, when the value p is greater than 1, the high-altitude parabolic behavior is determined, an alarm signal is sent out, corresponding multi-frequency-modulation-period echo data are recorded, and analysis is provided for subsequent parabolic householders to judge; and when p is less than 1, judging as an interference target, and continuing to monitor and update the p value until a judgment condition is met.
Referring to fig. 2, 3, and 4, as shown in the experimental results, in the embodiment of the present invention, the monitoring coefficient β is set to 0.36, the normalization coefficient τ is set to 2.4, the number n of effective points in the parabolic trajectory is 82, the value p of the calculated discriminant equation is 1.04, and the target can be determined as a high altitude parabolic target.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (10)
1. A high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting is characterized by comprising the following steps:
monitoring a suspicious parabolic area of the high-rise building in real time through a millimeter wave radar system;
sampling and processing radar echo signals to obtain a distance-Doppler detection track of a moving target;
adjusting parameters of the parabolic model, and fitting the parameters with the distance-Doppler detection track of the moving target to obtain a fitting error;
and setting a parabolic discriminant equation according to the fitting error, and judging whether the moving target is a parabolic target or not by using the parabolic discriminant equation.
2. The method for discriminating high altitude parabolic detection based on millimeter wave radar data fitting according to claim 1, wherein the millimeter wave radar system is installed on the ground on the side of the building, and the detection beam direction of the radar antenna is vertical to the upper direction and is parallel to the plane of the building wall.
3. The high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 1, wherein the millimeter wave radar system transmits signals by using a single antenna and receives signals by using four antennas which are arranged linearly and at the same interval; and the transmitting signal of the millimeter wave radar system is a linear frequency modulation continuous wave signal.
4. The high altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 3, wherein the chirp continuous wave signal employs a 76-81GHz frequency band.
5. The high altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 1, characterized in that radar echo signals are processed through two times of fast fourier transform, background interference points and noise are processed through a high pass filter and a constant false alarm algorithm, and then a range-doppler detection trajectory of a moving target is obtained through clustering.
6. The high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 5, characterized in that the high-pass filter is an FIR high-pass filter; the constant false alarm algorithm adopts a unit average method, a unit maximum value method or an ordered statistical method algorithm.
7. The high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 1, wherein the parabolic model is a classical mechanical parabolic model taking a moving object as a particle.
8. The high altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 1, characterized in that a least square method is adopted for fitting to obtain a simulated distance-doppler plot; calculating the mean square error of the actually detected point coordinates in the range-Doppler image and the sampling point coordinates of the simulated range-Doppler image, and taking the minimum value of the mean square error as a fitting error; and calculating and determining coefficients by using the residual square sum of the actual detection points and the fitting points and the regression square sum of all the detection points.
9. The high-altitude parabolic detection and discrimination method based on millimeter wave radar data fitting according to claim 1, wherein the parabolic discrimination equation is as follows:
p＝β·(R2+τ·ε+ψ(n))
wherein beta represents a monitoring coefficient, tau represents an error normalization coefficient, epsilon represents a fitting error, and R2Indicating the determination of the coefficients, n being the number of significant points in the satisfied parabolic trajectory, ψ (n) being the normalized limiting function.
10. The method for detecting and distinguishing the high altitude parabola according to the millimeter wave radar data fitting of claim 9, wherein the method comprises the steps of starting to calculate the value p of a parabola distinguishing equation in real time after a target is detected by a radar, and when p > -1 is satisfied, determining the high altitude parabola behavior, sending an alarm signal and recording data of corresponding multiple frequency modulation periods; and when p is less than 1, judging as an interference target, and continuously monitoring and updating the p value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115450453A (en) * | 2022-10-08 | 2022-12-09 | 长沙航空职业技术学院 | High-altitude object throwing, falling and falling person recognizing, early warning and blocking system and method thereof |
| CN116449291A (en) * | 2023-06-12 | 2023-07-18 | 中国人民解放军国防科技大学 | Passive omnidirectional sonar buoy supplementary feeding method and device based on positioning error analysis |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115450453A (en) * | 2022-10-08 | 2022-12-09 | 长沙航空职业技术学院 | High-altitude object throwing, falling and falling person recognizing, early warning and blocking system and method thereof |
| CN116449291A (en) * | 2023-06-12 | 2023-07-18 | 中国人民解放军国防科技大学 | Passive omnidirectional sonar buoy supplementary feeding method and device based on positioning error analysis |
| CN116449291B (en) * | 2023-06-12 | 2023-08-25 | 中国人民解放军国防科技大学 | Passive omnidirectional sonar buoy supplementary feeding method and device based on positioning error analysis |
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